Now showing items 1-4 of 4

    • Foveation-based Mechanisms Alleviate Adversarial Examples 

      Lou, Yan; Boix, Xavier; Roig, Gemma; Poggio, Tomaso; Zhao, Qi (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-01-19)
      We show that adversarial examples, i.e., the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in ...
    • The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors 

      O'Brien, Nicole; Latessa, Sophia; Evangelopoulos, Georgios; Boix, Xavier (Center for Brains, Minds and Machines (CBMM), 2018-11-01)
      The digital information age has generated new outlets for content creators to publish so-called “fake news”, a new form of propaganda that is intentionally designed to mislead the reader. With the widespread effects of the ...
    • Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results 

      Arend, Luke; Han, Yena; Schrimpf, Martin; Bashivan, Pouya; Kar, Kohitij; e.a. (Center for Brains, Minds and Machines (CBMM), 2018-11-02)
      Deep neural networks have been shown to predict neural responses in higher visual cortex. The mapping from the model to a neuron in the brain occurs through a linear combination of many units in the model, leaving open the ...
    • Theory of Deep Learning III: explaining the non-overfitting puzzle 

      Poggio, Tomaso; Kawaguchi, Kenji; Liao, Qianli; Miranda, Brando; Rosasco, Lorenzo; e.a. (arXiv, 2017-12-30)
      THIS MEMO IS REPLACED BY CBMM MEMO 90 A main puzzle of deep networks revolves around the absence of overfitting despite overparametrization and despite the large capacity demonstrated by zero training error on randomly ...